In this study, we aim to determine whether knowledge of the source of propagating disturbances can be exploited to improve the lane change behavior of CAVs. In particular, we explore the benefit of such systems in highway networks whereby throughput is restricted by the presence of a slow-moving, incident vehicle (Figure REF ). Assuming the incident is visible from a V2X sense, we augment the work of [1]} (see Section REF ) by introducing lane change incentives that inform the CAV to avoid, and if possible, overtake existing downstream congestion. Our model for Non-local Evasive Overtaking of downstream incidents, or NEO, is demonstrated to reduce the failure rate of CAVs attempting to reach certain routes in mandatory settings while improving the performance and reducing the disparity between humans and CAVs brought about by altruistic models in discretionary settings. Numerical results from various simulated errors also suggest that NEO is robust to limitations in sensing and sharing.
